Simon Willison writes about the release of Python 3.15 and the retirement of 3.10, highlighting new features and the practical implications for open-source maintainers.
- Explicit lazy imports (PEP 810) allow modules to be loaded only when symbols are first used, directly reducing startup time for CLI tools with heavy optional dependencies.
- PEP 798 introduces unpacking syntax for lists, sets, and dictionaries within comprehensions.
- A new low-overhead sampling profiler named Tachyon is now built into the standard library, capable of attaching to running processes and generating flame graphs.
- The experimental JIT compiler showed a 1.2x performance boost on specific benchmarks, marking the first time a meaningful increase was observed.
- Python 3.10 has reached end-of-life, allowing developers to adopt 3.11 features such as `tomllib`, `asyncio.TaskGroup`, and improved SQLite extensions.
- Explicit lazy imports (PEP 810) allow modules to be loaded only when symbols are first used, directly reducing startup time for CLI tools with heavy optional dependencies.
- PEP 798 introduces unpacking syntax for lists, sets, and dictionaries within comprehensions.
- A new low-overhead sampling profiler named Tachyon is now built into the standard library, capable of attaching to running processes and generating flame graphs.
- The experimental JIT compiler showed a 1.2x performance boost on specific benchmarks, marking the first time a meaningful increase was observed.
- Python 3.10 has reached end-of-life, allowing developers to adopt 3.11 features such as `tomllib`, `asyncio.TaskGroup`, and improved SQLite extensions.
YOLO Studio is a cloud-based workspace for orchestrating coding agents like Claude Code, Codex, and Muse Code in parallel, each running in isolated Linux sandboxes. The platform features a Kanban board where users describe work and a background runner breaks it into cards, executes them concurrently, and manages dependencies. Agents can operate in "YOLO Mode" to bypass per-step permission prompts, and finished work returns as commits with merge clearance options. The service includes live preview tiles, one-command deployment to yolo.host, scheduled loops for recurring tasks, and goal-based tracking where agents gather evidence against defined metrics.
- Supports 30+ frameworks out of the box for live preview
- YOLO Host provides one-command deployment with option to attach custom domains
- "Goals" feature ties agent work to measurable outcomes (e.g., doubling signup conversion) with an evidence-review step
- Dependency-aware dispatching ensures blocked-by edges are resolved in order before downstream cards start
- Supports 30+ frameworks out of the box for live preview
- YOLO Host provides one-command deployment with option to attach custom domains
- "Goals" feature ties agent work to measurable outcomes (e.g., doubling signup conversion) with an evidence-review step
- Dependency-aware dispatching ensures blocked-by edges are resolved in order before downstream cards start
Anthropic reports that new defensive measures have reduced the amount of Claude reasoning data harvested by illicit distillers by roughly 90 to 99%. Distillation attacks involve covert, industrial-scale campaigns where foreign labs use fraudulent account networks to extract Claude's hidden reasoning transcripts and train smaller models to mimic frontier capabilities. Anthropic has deployed three categories of defenses: "preserved thinking" restrictions that prevent reasoning blocks from being moved between conversations, models, or accounts; a two-stage constitutional classifier that detects extraction attempts with under 0.1% false positives; and usage-limit and account-ban policies for repeat offenders. The company acknowledges trade-offs, including forfeiting meaningful revenue and requiring some developers to update integrations.
- Zhipu distilled cyber capabilities from US models for GLM-5.3, which NIST assessed as the most cyber-capable open-weights model released to date
- One distillation network managed over 20,000 fraudulent accounts simultaneously
- A PLA-affiliated user leveraged the Kimi distillation pipeline to analyze CCTV surveillance of a specific individual
- Zhipu distilled cyber capabilities from US models for GLM-5.3, which NIST assessed as the most cyber-capable open-weights model released to date
- One distillation network managed over 20,000 fraudulent accounts simultaneously
- A PLA-affiliated user leveraged the Kimi distillation pipeline to analyze CCTV surveillance of a specific individual
The vllm-sr organization has released a collection of "Decision 3.0" models, described as open multimodal foundation decision models designed for zero-shot classification. The collection includes multiple model variants of varying sizes to suit different use cases.
- Available model sizes range from a 26B parameter flagship to a 0.6B parameter edge model
- The series includes d3, d3-flash, d3-mini, d3-nano, d3-lite, and d3-edge
- This is the third iteration in the Decision series, following Decision 1.0 and Decision 2.0
- The collection has received 78 upvotes and features 74 unique contributors
- Available model sizes range from a 26B parameter flagship to a 0.6B parameter edge model
- The series includes d3, d3-flash, d3-mini, d3-nano, d3-lite, and d3-edge
- This is the third iteration in the Decision series, following Decision 1.0 and Decision 2.0
- The collection has received 78 upvotes and features 74 unique contributors
Asif Razzaq writes that Microsoft released Microsoft-Decision-1, a model post-trained from Qwen3.5-9B that scores fixed answer options with calibrated probabilities rather than generating text. The model is designed for routing, classification, verification, and agent control, offering a 32,768-token context window, 85 ms p50 latency, and 83.5% average accuracy across 36 internal benchmarks. It is available as a hosted API through Microsoft Foundry and OpenRouter at $0.042 per million input tokens with free output tokens, but it has closed weights and text-only limitations.
- Latency comparisons against competitors are disputed because Microsoft used its own measurement while citing JevBench adjusted figures for others
- The model achieves zero decision flips under paraphrasing, reversal, or option-shuffling perturbations
- Internal Microsoft use cases report 14-100x speed advantages over GPT-6 Sol in feedback sorting and quality control
- Latency comparisons against competitors are disputed because Microsoft used its own measurement while citing JevBench adjusted figures for others
- The model achieves zero decision flips under paraphrasing, reversal, or option-shuffling perturbations
- Internal Microsoft use cases report 14-100x speed advantages over GPT-6 Sol in feedback sorting and quality control
Dan Russell writes about distinguishing between true, correct, and valid claims and offers a six-step verification routine for navigating misinformation. He notes that people often confuse fluency with factuality and that each of the three questions—truth, correctness, and validity—fails in distinct ways requiring different checks. His practical steps include pinning down the claim, noticing emotional reactions, tracing evidence upstream, reading laterally, triangulating with independent sources, and calibrating confidence. He also addresses the specific challenge of verifying what AI tells you, noting that fluent, confident outputs break usual credibility cues, and recommends treating citations as claims to be checked rather than evidence to be trusted.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
- The distinction between true, correct, and valid is central: a claim can pass one test and fail others (e.g., a real statistic from the wrong year is correct in form but fails on truth).
- Wineburg and McGrew's research found professional fact-checkers outperformed historians and Stanford undergraduates at evaluating websites, largely because they read laterally rather than staying on a single page.
- AI errors cluster in specifics like dates, names, page numbers, and exact figures, while the gist is often right—making them a correctness failure rather than a truth failure.
- The SIFT method (Stop, Investigate the source, Find better coverage, Trace claims to the original) packages lateral reading moves into a teachable framework.
tarwin writes about tinyjs, a JavaScript-based desktop app framework that leverages the OS's native webview and a lightweight txiki.js runtime to produce roughly 6 MB binaries across macOS, Windows, and Linux. Unlike Electron or Tauri, it requires no compilation step, bundles no browser engine, and uses no localhost ports, relying instead on Unix sockets or named pipes for secure inter-process communication between the webview and backend.
- The backend provides full system access including files, sockets, FFI, and SQLite.
- macOS is considered stable, while Windows and Linux are currently in beta.
- Developers can scaffold projects with React, Vue, Svelte, or other frameworks using npm, pnpm, or Vite.
- The framework includes a built-in updater that verifies SHA256 hashes and code signatures before swapping in new versions.
- The backend provides full system access including files, sockets, FFI, and SQLite.
- macOS is considered stable, while Windows and Linux are currently in beta.
- Developers can scaffold projects with React, Vue, Svelte, or other frameworks using npm, pnpm, or Vite.
- The framework includes a built-in updater that verifies SHA256 hashes and code signatures before swapping in new versions.
Michal Sutter writes that the vLLM Semantic Router team has released Decision 3.0, a family of five open multimodal decision models ranging from 0.85B to 26.09B parameters. Fine-tuned on Qwen base models, these models accept text, JSON, and images and return calibrated probabilities for typed questions rather than generated text, making them useful for classification and routing tasks.
- All models carry a vision encoder and support multiple image inputs up to 1.6 megapixels each.
- The 9B d3-flash outperforms the previous generation's 27B model, showing significant efficiency gains.
- Benchmarks are self-reported against the Jev Decision Index, with d3 leading at 64.1 on text and 71.6 on vision.
- Median text latency ranges from 21.7 ms for d3-nano to 84 ms for d3 on a single AMD Instinct MI325X GPU.
- All models carry a vision encoder and support multiple image inputs up to 1.6 megapixels each.
- The 9B d3-flash outperforms the previous generation's 27B model, showing significant efficiency gains.
- Benchmarks are self-reported against the Jev Decision Index, with d3 leading at 64.1 on text and 71.6 on vision.
- Median text latency ranges from 21.7 ms for d3-nano to 84 ms for d3 on a single AMD Instinct MI325X GPU.
Simon Batt writes about unidex, an open-source operating system for the 1.54" Waveshare ESP32-S3 ePaper display that turns the tiny board into a pocket productivity device with a Tamagotchi-like virtual pet. It displays the next class with a live countdown, converts voice notes to Markdown for Obsidian, and allows the pet to interact with other users' pets.
- Installable directly from a browser
- Includes a WiFi collection game and four one-button games
- Free to fork and customize for non-commercial purposes
- Installable directly from a browser
- Includes a WiFi collection game and four one-button games
- Free to fork and customize for non-commercial purposes
This documentation introduces how to fine-tune large language models into decision models using Unsloth. These models are designed to make decisions with calibrated probabilities rather than simply generating text. Users can train models like Qwen or Gemma to read input, score provided options, and return a specific choice. The process is supported through the Unsloth Desktop app, free notebooks, or custom Python code, and the resulting model can be integrated directly into the Decision API for practical use.
